(1) Field of Invention
The present invention relates to a bio-inspired system for feature detection and, more particularly, to a bio-inspired system for feature detection with spiking dynamics.
(2) Description of Related Art
Learning feature detectors represents one of the most fundamental tasks for neural networks. For current purposes, it refers to the ability to compressively code frequently occurring input patterns. There are many approaches to learning feature detectors. For instance, Instar learning was one of the first neural network models of learning feature detectors. Further, spike-timing-dependent-plasticity (STDP) has gained recent popularity due to its prevalence throughout the brain. “Spike” is a term used to describe input action potentials in a neural network. Instar STDP learning is an attempt to unify both learning rules.
Instar learning is Hebbian learning with post-synaptic gating (see Literature Reference No. 12). Instar learning has the properties of online learning with analog sensitivity to input patterns. As originally defined in Instar learning, rate-coded neurons trace pre-synaptic activities, typically defined by mean firing rates or membrane potentials. Instar learning in the spiking domain has not been previously demonstrated.
STDP is a temporally asymmetric Hebbian learning rule. When pre-synaptic firing precedes post-synaptic firing, peak synaptic conductances or weights are said to increase. On the other hand, when pre-synaptic firing follows post-synaptic firing, peak synaptic conductances or weights are said to decrease. STDP causes an intrinsic normalization of pre-synaptic weights and post-synaptic firing rates, but often leads to bimodal weight distributions (see Literature Reference No. 29). Gorchetchnikov et al. created Instar STDP learning where weights track STDP efficacy values (see Literature Reference No. 8). Although spatially and temporally local, complicated neuronal and synaptic dynamics prevent ready hardware realization.
Although learning feature detectors has been shown in both rate- and spike-coding domains, a continuing need exists for translating the functional properties of rate-coded models into the spiking domain, thereby opening the door for automated conversion across domains.